Although firms rely on employees’ innovative work behaviour and effective leadership to achieve service innovation performance, these relations remain underexplored, especially regarding digital leadership. We conceptualise a digital leader’s capabilities and explore influences on innovative work behaviour and service innovation performance, using the dynamic capabilities view as a theoretical lens. Applying a multi-method exploratory research design, our qualitative results, based on 34 expert interviews, deliver a taxonomy of digital leadership capabilities along three dimensions. With 249 survey participants, we quantitatively tested dimensional influences individually (multidimensional view) and collectively (unidimensional view) using structural equation modelling. In line with our mediation results, both views are significantly positively related to innovative work behaviour; still, only the unidimensional view significantly influences service innovation performance. Our results underpin the comprehensive character of digital leadership capabilities contributing to innovation research with a new “antecedal” perspective. We also provide practical relevance by revealing innovation-effective leadership capabilities.

This paper provides first empirical evidence on the effect of geopolitical risks in fossil fuel supplier countries on renewable energy diffusion in fossil fuel importing countries and the mediating roles of rising electricity prices and high import dependence. For this end, aggregate measures of geopolitical risk that countries are exposed to through fossil fuel imports are determined. This is done by combining detailed data on bilateral trade patterns for coal, oil, and natural gas of 37 countries in Europe with that on geopolitical risks in supplier countries. Using an instrumental variable approach, the study reveals that geopolitical risks in supplier countries tended to foster renewable energy diffusion in Europe during the period 1991–2021. The effects are especially pronounced for geopolitical risks related to coal and natural gas imports, while the importance of risks related to particular fossil fuels differed for the build-up of the individual renewable energies, i.e. wind, solar, and biomass. Rising electricity prices and high import dependence, particularly for coal, partially amplified the effects on renewable energy diffusion. Despite the high import dependence, natural gas appears to have played in part a role as a bridging technology for energy transition.

This study explores the concept of massive and rapid business scaling (MRBS) in the context of digital start-ups by identifying 20 factors clustered into seven core drivers. Through inductive qualitative research, the study builds on 53 semi-structured interviews with founders, executives, and advisors, leading to the development of a framework that uncovers seven core drivers of MRBS contributing to the scaling process. These core drivers are as follows: 1) scanning the environment and recognizing opportunities, 2) iteratively adjusting the business model with an asset-light structure, 3) achieving operational excellence through digitization, 4) building an efficient and entrepreneurial workforce combined with leadership and vision, 5) leveraging internal resources to strengthen positioning and expand the market, 6) attracting capital to facilitate growth realization, and 7) cultivating organizational agility and a transformation culture. While core drivers one to five imply a processual nature, the sixth and seventh core drivers serve as a foundation for MRBS. Moreover, this study outlines several areas of tension within the process of MRBS. Therefore, the study provides valuable insights for scholars and practitioners.

Research in artificial intelligence and business model innovation is flourishing. Nevertheless, the current discussion lacks an overarching understanding of, and thus has not sufficiently addressed, the interface between artificial intelligence-enabled business model innovation and the critical role of top management. Although a paradigm shift affecting top management is already occurring, extant management literature is limited, especially in terms of primary research. Accordingly, this study explores how top management can encourage and facilitate artificial intelligence-enabled business model innovation. We utilized an inductive approach and conducted semistructured interviews with 47 practitioners to develop a grounded theory. The developed framework consists of five top management competencies and eight top management roles. Overall, our study contributes to research in business model innovation theory, revealing that top management requires a specific skill set to carry out their roles and fulfill expectations.

Purpose The article discusses the current relevance of artificial intelligence (AI) in research and how AI improves various research methods. This article focuses on the practical case study of systematic literature reviews (SLRs) to provide a guideline for employing AI in the process. Design/methodology/approach Researchers no longer require technical skills to use AI in their research. The recent discussion about using Chat Generative Pre-trained Transformer (GPT), a chatbot by OpenAI, has reached the academic world and fueled heated debates about the future of academic research. Nevertheless, as the saying goes, AI will not replace our job; a human being using AI will. This editorial aims to provide an overview of the current state of using AI in research, highlighting recent trends and developments in the field. Findings The main result is guidelines for the use of AI in the scientific research process. The guidelines were developed for the literature review case but the authors believe the instructions provided can be adjusted to many fields of research, including but not limited to quantitative research, data qualification, research on unstructured data, qualitative data and even on many support functions and repetitive tasks. Originality/value AI already has the potential to make researchers’ work faster, more reliable and more convenient. The authors highlight the advantages and limitations of AI in the current time, which should be present in any research utilizing AI. Advantages include objectivity and repeatability in research processes that currently are subject to human error. The most substantial disadvantages lie in the architecture of current general-purpose models, which understanding is essential for using them in research. The authors will describe the most critical shortcomings without going into technical detail and suggest how to work with the shortcomings daily.

We study the asymptotic stability in replicator dynamics derived from TU games using the dual Lovász-Shapley value and the Shapley2 value for non-negatively weighted games. In particular, we provide a complete description of asymptotically stable population profiles in both dynamics. In the dual Lovász-Shapley replicator dynamic, for example, asymptotically stable populations for simple monotonic games correspond to their minimal blocking coalitions.

Despite service productivity’s scholarly prominence and practical relevance, past research in marketing has primarily adopted isolated perspectives from which disjointed empirical findings reign supreme. As the acquisition of knowledge about service productivity accelerates, the collective evidence becomes more interdisciplinary but also more fragmented. This study uses a meta-analysis to integrate the substantial empirical record on service productivity. We formulate hypotheses on the moderators of service productivity-determinant relationships and meta-analyze 77 articles, relying on 81 independent samples with a cumulative sample size of 30,238 participants to test our predictions. Our meta-analysis provides empirical evidence that service quality and internal efficiency must be considered jointly, not in isolation, to maximize profitability. Thus, relying on one aspect in isolation is less appropriate for measurement purposes and might not lead to positive outcomes. This important finding should concern service scholars and managers because falling profit margins require service firms to move beyond the traditional manufacturing productivity that separates service quality from internal efficiency and consider service productivity as a profitability concept. In sum, our findings provide a viable model to explain the main service productivity determinants and moderating variables, offering valuable insights for practitioners that aim to deliver cost-efficient service quality and promising future research directions.

Since its inception over two decades ago, the theory of disruptive innovation has sparked heated discussions. Especially because of the increasing importance of societal influences and novel forms of competition and technology, questions about its theoretical value and practical relevance remain. Researchers have focused on firm-internal factors of disruptive innovations to resolve discussions about the validity of the theory. However, the literature lacks an integrated understanding of contextual factors, such as demand, market structure, culture, and regulation, that influence disruptive innovation because of its dispersed, fragmented character across disciplines. Our study addresses this fragmentation and lack of integrated understanding by systematically reviewing 62 articles. The study makes three main contributions. First, we integrate and synthesize the literature on contextual factors of disruptive innovations. Second, we derive a three-phase framework of contextual factors: (1) disruptive susceptibility, (2) emergence and diffusion, and (3) endgame and outcome. Third, we contribute to resolving discussions about the theory's core elements and its predictive value by showing how, depending on the societal, cultural, or market context, the implications of the theory can change. Overall, this article shows how disruptive innovation can start, and be started, by social change. We conclude by suggesting areas for future research.

Using a sample of 18,225 global buyouts, we find that management buyouts (MBOs) are significantly more likely to occur if economic policy uncertainty (EPU) increases. This finding is consistent with the idea that EPU provides an opportunity for insiders to capitalize on private information and time the market. Further results suggest that market timing pays off on average. We find that MBOs achieve more favorable buyout prices and greater post-buyout operating improvements than institutional buyouts during times of high EPU. Our results hold when exploiting close national election races as a quasi-natural experiment for EPU.

Venture capital (VC) often involves complex equity contracts with so-called preferential rights affecting the allocation of exit proceeds among different share classes and investors. We structure exit-relevant preferential rights in a two-dimensional framework and develop a contingent claims model that allows for ex-ante valuation of separate shareholdings. The model generates insights on the valuation effects of varying setups in VC financing and indicates considerable mispricing potential of VC investments when applying commonly used heuristics such as the most recent funding round. Applying the model to a sample of ventures indicated an average ’overvaluation’ on a per-share basis of 26.7%, with common stocks and early-stage investments being the most affected. In addition, our analysis provides different implications regarding the effects of preferential right structuring for early and late stage investors.